Stateless vs Stateful Fuzzing

Fuzz testing involves providing invalid, unexpected, or random data as inputs. It is crucial in identifying vulnerabilities. This method can be broadly categorized into two types: stateful fuzzing and stateless (i.e. invariant or property) fuzzing. Each approach has its benefits and limitations, offering different insights into the security posture of smart contracts.

Stateful Fuzzing

Stateful fuzzing involves testing a smart contract by considering its state across multiple transactions. This method not only provides random inputs but also sequences of transactions that interact with the contract in various states, simulating realistic scenarios that the contract might face once deployed.


  1. Comprehensive Analysis: By considering the contract’s state over time, stateful fuzzing can uncover vulnerabilities that only appear under certain conditions or sequences of actions, providing a deeper understanding of potential security issues.
  2. Real-World Simulation: Stateful fuzzing mimics real-world interaction with the contract, including sequences of transactions from multiple users, which can reveal complex vulnerabilities related to state changes and interactions.


  1. Complexity and Resource Intensity: Maintaining and tracking the state of the contract increases the complexity of the fuzzing process and demands more computational resources, making it potentially slower and more difficult to execute.
  2. Challenges in Setup: Crafting effective stateful fuzz tests requires a thorough understanding of the contract’s logic and potential states, necessitating more sophisticated setup and configuration.

Stateless (Invariant or Property) Fuzzing

Stateless fuzzing, in contrast, focuses on the contract’s properties or invariants—conditions that should always hold true, regardless of the contract’s state. This approach tests these properties by providing random inputs to the contract and checking if the properties still hold.


  1. Simplicity and Speed: Stateless fuzzing is generally simpler to implement and faster to execute than stateful fuzzing, as it does not require tracking the contract’s state over multiple transactions.
  2. Focus on Invariants: This method is effective in verifying that key invariants of the contract hold under a wide range of conditions, helping to ensure the contract’s integrity and correctness.


  1. Limited Scope: While stateless fuzzing is excellent for testing specific properties, it may not fully capture vulnerabilities that arise from complex interactions or state transitions over time.
  2. Potential for Overlooking Contextual Vulnerabilities: Since stateless fuzzing does not account for the contract’s state across transactions, it might overlook vulnerabilities that are dependent on specific sequences of actions or states.

Choosing Between Stateful and Stateless Fuzzing

The choice between stateful and stateless fuzzing depends on several factors, including the complexity of the smart contract, the resources available for testing, and the specific security concerns at hand. In practice, a comprehensive security assessment often involves a combination of both methods to leverage their respective strengths:

  • Stateful fuzzing is particularly suited for complex contracts with intricate state management and interactions, where vulnerabilities might only emerge under specific conditions.
  • Stateless fuzzing is ideal for quickly verifying the fundamental properties and invariants of a contract across a broad range of inputs, especially when simplicity and speed are priorities.


Both stateful and stateless fuzzing play vital roles in the security testing of smart contracts, each offering unique advantages while also facing certain limitations. By understanding these differences and applying the appropriate fuzzing techniques, developers and auditors can significantly enhance the security and reliability of smart contracts on blockchain networks. As the landscape of smart contract development continues to evolve, the integration of both stateful and stateless fuzzing approaches will be crucial in identifying and mitigating potential vulnerabilities, thereby ensuring the integrity and trustworthiness of blockchain applications.